A Survey on Nutri-Guide: An Intelligent System for Personalized Dietary Management and Nutritional Analysis

Abstract

Maintaining a balanced diet is central to preventing lifestyle-related and chronic conditions such as obesity, diabetes, cardiovascular disease, and hypertension, yet conventional dietary assessment methods — food diaries, food-frequency questionnaires, and 24-hour dietary recall — are time-consuming, burdensome, and prone to memory-based bias. Recent advances in artificial intelligence, computer vision, and deep learning have introduced automated, image-based alternatives, in which a photograph of a meal, rather than a verbal or written description, becomes the primary data source for dietary assessment. This paper reviews and synthesizes recent research on image-based food recognition, food segmentation, portion and volume estimation, calorie and nutrient calculation, and personalized dietary recommendation, drawing on a methodological review of classification and volume-estimation algorithms, a deep-learning pipeline study covering dataset construction and nutrient-database matching, and an applied case system (IntelligentDine) that fine-tunes a 150-layer ResNet on the Food-101 dataset to jointly perform food identification and calorie regression, reporting 97.30% training accuracy and 98.02% validation accuracy. The reviewed studies show that convolutional neural networks, transfer-learning architectures such as ResNet and EfficientNet, and benchmark datasets such as Food-101 have substantially improved food-classification performance, while portion and volume estimation, dataset diversity (particularly for Indian and other regional cuisines), real-world robustness, privacy, and mobile deployability remain comparatively unresolved. Building on these findings, this paper proposes a unified conceptual framework that integrates image acquisition, preprocessing, food detection and classification, portion/volume estimation, nutrient-database lookup, and personalized health-based recommendation into a single intelligent dietary-assessment system, and outlines directions for future research toward accurate, real-time, and personalized dietary guidance.